105
tions, and requires explication and empirical investigation of the hypothesized
connections between the objects and processes under investigation and measurement results. Such awareness and acknowledgment are only possible to the extent
to which claims are made explicit, and explication of claims requires a coherent
semantics with which claims can be formulated.
In summary, a model-based realist perspective on measurement at the same time
maintains something of each of the previous positions but rejects their most radical
aspects:
• It accepts from realism the position that some properties do exist in the world,
and are not just human constructs, but rejects the metaphysical claim that values
of properties exist independently of our models (illustrated in Fig. 4.7 by “reality” on the left-hand side).
• It accepts from non-realist empiricism (positivism, operationalism, representationalism, etc.) that empirical data can provide the evidential foundation for
knowledge, but rejects the foundationalist claim that generic observation can
have such a role, which is instead vested only in the empirical component of
measurement systems, which are specifically designed for this purpose; moreover, it accepts that such evidence is always revisable (illustrated in Fig. 4.7 by
“Model of the measurement process”).
• It accepts from pragmatism (and relativism) that measurement is a designed-onpurpose process and that models in measurement are unavoidable, but rejects the
possible conclusion that “anything goes” and that the quality of measurement
results can be evaluated only a posteriori, in terms of the effectiveness of their
application (illustrated in Fig. 4.7 by the first three lenses on the left).
This perspective grounds the interpretation on an ontology and an epistemology of
properties that is developed further in the chapters that follow.
References
American Educational Research Association (AERA) and Other Two Organizations. (2014).
Standards for psychological and educational tests. Washington, DC: American Educational
Research Association (AERA), American Psychological Association (APA), and National
Council for Measurement in Education (NCME).
Austin, J. L. (1975). How to do things with words (Vol. 88). Oxford: Oxford University Press.
Babbie, E. (2013). The practice of social research (13th ed.). Wadsworth: Belmont.
Bell, S. (1999). A beginner’s guide to uncertainty of measurement. Measurement Good Practice
Guide No. 11 (Issue 2). National Physical Laboratory. Retrieved from www.dit.ie/media/physics/documents/GPG11.pdf
Bentley, J. P. (2005). Principles of measurement systems. New York: Pearson.
Bickhard, M. H. (2001). The tragedy of operationalism. Theory and Psychology, 11(1), 35–44.
Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 36, 35–37.
Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics.
Cambridge: Cambridge University Press.
Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71(3), 425–440.
References
tions, and requires explication and empirical investigation of the hypothesized
connections between the objects and processes under investigation and measurement results. Such awareness and acknowledgment are only possible to the extent
to which claims are made explicit, and explication of claims requires a coherent
semantics with which claims can be formulated.
In summary, a model-based realist perspective on measurement at the same time
maintains something of each of the previous positions but rejects their most radical
aspects:
• It accepts from realism the position that some properties do exist in the world,
and are not just human constructs, but rejects the metaphysical claim that values
of properties exist independently of our models (illustrated in Fig. 4.7 by “reality” on the left-hand side).
• It accepts from non-realist empiricism (positivism, operationalism, representationalism, etc.) that empirical data can provide the evidential foundation for
knowledge, but rejects the foundationalist claim that generic observation can
have such a role, which is instead vested only in the empirical component of
measurement systems, which are specifically designed for this purpose; moreover, it accepts that such evidence is always revisable (illustrated in Fig. 4.7 by
“Model of the measurement process”).
• It accepts from pragmatism (and relativism) that measurement is a designed-onpurpose process and that models in measurement are unavoidable, but rejects the
possible conclusion that “anything goes” and that the quality of measurement
results can be evaluated only a posteriori, in terms of the effectiveness of their
application (illustrated in Fig. 4.7 by the first three lenses on the left).
This perspective grounds the interpretation on an ontology and an epistemology of
properties that is developed further in the chapters that follow.
References
American Educational Research Association (AERA) and Other Two Organizations. (2014).
Standards for psychological and educational tests. Washington, DC: American Educational
Research Association (AERA), American Psychological Association (APA), and National
Council for Measurement in Education (NCME).
Austin, J. L. (1975). How to do things with words (Vol. 88). Oxford: Oxford University Press.
Babbie, E. (2013). The practice of social research (13th ed.). Wadsworth: Belmont.
Bell, S. (1999). A beginner’s guide to uncertainty of measurement. Measurement Good Practice
Guide No. 11 (Issue 2). National Physical Laboratory. Retrieved from www.dit.ie/media/physics/documents/GPG11.pdf
Bentley, J. P. (2005). Principles of measurement systems. New York: Pearson.
Bickhard, M. H. (2001). The tragedy of operationalism. Theory and Psychology, 11(1), 35–44.
Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 36, 35–37.
Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics.
Cambridge: Cambridge University Press.
Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71(3), 425–440.
References